Optimal design for on-farm strip trials -- systematic or randomised?

TL;DR

This paper compares systematic and randomised designs for large on-farm experiments, finding that systematic designs are preferable when modeling quadratic spatial variation, especially for mapping optimal input levels.

Contribution

The study introduces a comparison framework for systematic versus randomised designs in large on-farm trials using geographically weighted regression and simulations.

Findings

Systematic designs outperform randomised ones for quadratic models.

No significant difference between designs when using linear models.

Systematic designs are more robust for mapping optimal input levels.

Abstract

There is no doubt on the importance of randomisation in agricultural experiments by agronomists and biometricians. Even when agronomists extend the experimentation from small trials to large on-farm trials, randomised designs predominate over systematic designs. However, the situation may change depending on the objective of the on-farm experiments (OFE). If the goal of OFE is obtaining a smooth map showing the optimal level of a controllable input across a grid made by rows and columns covering the whole field, a systematic design should be preferred over a randomised design in terms of robustness and reliability. With the novel geographically weighted regression (GWR) for OFE and simulation studies, we conclude that, for large OFE strip trials, the difference between randomised designs and systematic designs are not significant if a linear model of treatments is fitted or if the spatial variation is not taken into account. But for a quadratic model, systematic designs are superior to randomised designs.